Testing of features for fatigue detection in EOG

Article type: Research Article

Authors: Němcová, Andrea^{a; *} | Janoušek, Oto^a | Vítek, Martin^a | Provazník, Ivo^{a; b}

Affiliations: [a] Department of Biomedical Engineering, Brno University of Technology, Technická 12, 616 00 Brno, Czech Republic | [b] Department of Physiology, Faculty of Medicine, Masaryk University, Kamenice 753/5, 625 00 Brno, Czech Republic

Correspondence: [*] Corresponding author. E-mail: andrea.nemcova@phd.feec.vutbr.cz.

Abstract: The article deals with the testing of features for fatigue detection in electrooculography (EOG) records. An optimal methodology for EOG signal acquisition is described; the Biopac data acquisition system was used. EOG signals were being recorded while 10 volunteers were watching prepared scenes. Three scenes were created for this purpose – a rotating ball, a video of driving a car, and a cross. Recorded EOG signals were processed and 20 features were extracted. The features involved blinks, slow eye movement (SEM), rapid eye movement (REM), eye instability, magnitude, and periodicity. These features were statistically tested and discussed in terms of fatigue detection ability. Some of the features were compared with published results. Finally, the best features – fatigue indicators – were selected.

Keywords: Biopac, blink, electrooculography, REM, scenes, SEM

DOI: 10.3233/BME-171683

Journal: Bio-Medical Materials and Engineering, vol. 28, no. 4, pp. 379-392, 2017